Solving optimization problems in the design of fixtures for flexible manufacturing systems

Khusainov, R. M.; Mindiyarov, R R; Petrovetskiy, B I

doi:10.1088/1757-899x/971/3/032063

Cited by 1 publication

(2 citation statements)

References 16 publications

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“…B represents the generated neighbors, P represents the generated initial population, F 1 and F 2 are the two extreme points of the Pareto front in the objective space, and σ adopts the diversity maintenance strategy in [2] by selecting the generated parent from the neighbors when it is less than σ and otherwise selecting the generated parent from the population. For the reproduction operation, this study uses the optimized Lévy algorithm nested polynomial mutation to generate offspring; the g tn Chebyshev discriminant method representing the NBI type introduced in part 2 is used, where r p and λ can be calculated by formula (6), and n r updates the upper bound of the neighbors.…”

Section: Global Search and Adaptive Control Abilitymentioning

confidence: 99%

“…Thus, MOEA/D has been widely used in complex data analysis [3,4]. In recent years, with the development of Industry 4.0, many combinatorial optimization problems about large-scale and multiple level datasets have arisen [5,6], while the MOEA/D algorithm has been introduced to address this problems, which is a kind of complex data analysis MOPs. For example, in the MOEA/D-Lévy algorithm [7], a Lévy flight is a short-distance hopping exploratory search strategy with occasional long-distance development search.…”

Section: Introductionmentioning

confidence: 99%

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An Improved MOEA/D Algorithm for Complex Data Analysis

Qian

Liu

Lin

et al. 2021

Wireless Communications and Mobile Computing

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There are a large number of multiple level datasets in the Industry 4.0 era. Thus, it is necessary to utilize artificial intelligence technology for the complex data analysis. In fact, the technology often suffers from the self-optimization issue of multiple level datasets, which is taken as a kind of multiobjective optimization problem (MOP). Naturally, the MOP can be solved by the multiobjective evolutionary algorithm based on decomposition (MOEA/D). However, most existing MOEA/D algorithms usually fail to adapt neighborhood for the offspring generation, since these algorithms have shortcomings in both global search and adaptive control. To address this issue, we propose a MOEA/D with adaptive exploration and exploitation, termed MOEA/D-AEE, which adopts random numbers with a uniform distribution to explore the objective space and introduces a joint exploitation coefficient between parents to generate better offspring. By dynamic exploration and joint exploitation, MOEA/D-AEE improves both global search ability and diversity of the algorithm. Experimental results on benchmark data sets demonstrate that our proposed approach achieves global search ability and diversity in terms of the population distribution than state-of-the-art MOEA/D algorithms.

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Section: Global Search and Adaptive Control Abilitymentioning

confidence: 99%